Empirical Evaluation of the Impact of Class Overlap on Software Defect Prediction

Gong, Lina; Jiang, Shujuan; Wang, Rongcun; Jiang, Li

doi:10.1109/ase.2019.00071

Cited by 31 publications

(24 citation statements)

References 28 publications

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“…The whole dataset was then randomly sampled several times to create an ensemble classification model. Jiang et al [9] proposed an improved k-means clustering cleaning approach (IKMCCA) to solve the class overlap issue and the class imbalance problem. The experiment revealed that it is better to consider both the class overlap problem and the class imbalance problem.…”

Section: Class Overlapmentioning

confidence: 99%

“…Similar instances may overlap densely in the space based on different features. The class overlap issue has been investigated in other application domains, such as software defect prediction [9]. That is to say, for EEG-based early epileptic seizure detection, different epilepsy seizures may have the same feature.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the class imbalance problem often accompanies the class overlap issue. The current commonly used strategies include the neighborhood cleaning rule learning (NCR), and the improved k-means clustering cleaning approach (IKMCCA) [9]. The NCR method removes the conflicting majority instances to solve the class overlap issue, while the minority instances are not processed to achieve the balance between the majority class and the minority class [12].…”

Section: Introductionmentioning

confidence: 99%

“…The IKMCCA method is based on the standard k-means algorithm. For each cluster, the majority instances and the minority instances are eliminated according to the ratio between the minority instances and the majority instances [9].…”

Section: Introductionmentioning

confidence: 99%

“…To achieve an objective estimation of the class overlap issue, we conduct the experiments on two widely used EEG datasets and compare our proposed SNCR strategy with a state-of-the-art data clean strategy [9]. Performance evaluation measures (i.e., AUC, recall, and F1) are used to compare the performance of different strategies.…”

Section: Introductionmentioning

confidence: 99%

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Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Chen

Li³

et al. 2020

IEEE Access

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Important physiological information is hidden in electroencephalography (EEG), which can reflect the human brain's activity. EEG, which is a kind of complicated signal, can be used for epileptic seizure detection and epilepsy diagnosis via machine learning. A large amount of effort, including raw signal preprocessing and data preprocessing for machine learning, is required for constructing high-quality training datasets because the classification performance highly depends on high-quality data. Feature extraction has been widely used in EEG-based early epileptic seizure detection. Due to the complexity of data collection and labeling, some of the training instances are inevitably mislabeled. That means some similar instances have different labels. This is called the issue of class overlap, which leads to a poor class boundary for classification models and makes constructing a high-quality classification model more difficult. However, the previous studies investigating the impact of the class overlap for EEG data is quite limited. Our goal is to investigate the impact of the class overlap on EEG-based early epileptic seizure detection. We propose a special neighborhood cleaning rule (SNCR) to solve the class overlap issue. To alleviate the class overlap issue, we conduct large-scale experiments on two widely-used EEG datasets and compare our proposed SNCR strategy with a state-of-the-art data clean strategy, i.e., the improved k-means clustering cleaning approach (IKMCCA). The experimental results show that the classification model can achieve significantly better performance in terms of AUC, recall, and F1 metrics when using our proposed SNCR strategy. Therefore, for EEG-based early epileptic seizure detection, we recommend researchers to apply the SNCR strategy to mitigate the class overlap issue and use the SNCR strategy to perform data preprocessing in a future related study.

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Section: Class Overlapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Chen

Li³

et al. 2020

IEEE Access

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Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Yu,

Zhu,

Han

et al. 2023

J Software Evolu Process

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Cross‐version defect prediction (CVDP) for evolutionary projects has attracted much attention from researchers in recent years. For multiple versions of an object‐oriented project, the degree of evolution (e.g., the degree of class change) between successive versions can reflect the differences between versions, which could affect the performance of CVDP. Therefore, how to measure the degree of evolution between successive versions and explore the correlations with the performance of CVDP are very important for software defect prediction. Based on the successive versions of evolutionary projects, this paper proposes six evolutionary measures from three aspects of class change, metric change, and label change, including the Ratio of New Classes (RNC), the Ratio of Deleted Classes (RDC), the Average Ratio of Metric Change (ARMC), the Ratio of Label Changed Classes (RLCC), the Ratio of Unchanged Classes (RUC), and the Ratio of Interference Classes (RIC). An empirical study was conducted on 40 versions of 11 object‐oriented projects from the PROMISE repository. Precision, Recall, F‐measure, and AUC were used as the performance indicators. Three correlation approaches (Pearson, Spearman, and Kendall) are applied to show the correlations between evolutionary measures and the performance of CVDP. The statistical results show that RNC, RDC, and RUC show no correlation with four performance indicators. ARMC shows weak or medium positive correlations with Recall and F‐measure. RLCC and RIC show very strong or strong negative correlations with Recall and F‐measure. The results indicate that the correlations between the proposed evolutionary measures and the performance of CVDP are different, which can guide the training set selection of CVDP.

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Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs)

Borah¹,

Kama²,

Rakshit³

et al. 2022

Cognitive Informatics and Soft Computing

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Empirical Evaluation of the Impact of Class Overlap on Software Defect Prediction

Cited by 31 publications

References 28 publications

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs)

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